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VARIANT CALLING IN SINGLE MOLECULE SEQUENCING USING A CONVOLUTIONAL NEURAL NETWORK
VARIANT CALLING IN SINGLE MOLECULE SEQUENCING USING A CONVOLUTIONAL NEURAL NETWORK
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机译:使用卷积神经网络进行单分子测序的变量计算
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摘要
Systems and methods for variant calling in single molecule sequencing from a genomic dataset using a convolutional deep neural network. The method includes: transforming properties of each of the variants into a multi-dimensional tensor; passing the multi-dimensional tensors through a trained convolutional deep neural network to predict categorical output variables, the convolutional deep neural network minimizing a cost function iterated over each variant, the convolutional deep neural network trained using a training genomic dataset including previously identified variants, the convolutional neural network including: a plurality of pooled convolutional layers and at least two fully-connected layers connected sequentially after the last of the pooled convolutional layers, the at least two fully-connected layers comprising a second fully-connected layer connected sequentially after a first fully-connected layer; and outputting the predicted categorical output variables. In some cases, the categorical output variables include an alternate base, zygosity, variant type, and length of an indel.
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